Autoimmune Disease-Related Hub Genes are Potential Biomarkers and Associated with Immune Microenvironment in Endometriosis.

Endometriosis, a common gynecological condition, can cause symptoms such as dysmenorrhea, infertility, and abnormal bleeding, which can negatively affect a woman's quality of life. In the current study, the pathophysiological mechanisms of endometriosis are unknown, but this study suggests that endometriosis is associated with dysregulation of the autoimmune system. This study identify hub genes involved in the prevalence, identification and diagnostic value of endometriosis and autoimmune diseases, and explore the central genes and immune infiltrates, the diagnosis of endometriosis provides a new sight of thinking about diagnosis and treatment. The relevant datasets for endometriosis GSE141549, GSE7305 and autoimmune disease-related genes (AIDGs) were downloaded from online database. Using the "limma" package and WGCNA to screen out the autoimmune disease related genes and endometriosis related genes, the autoimmune disease gene-related differential genes (AID-DEGs) progressive GO, KEGG enrichment analysis, and then using the protein interaction network and Cytoscape software to select hub genes (CXCL12, PECAM1, NGF, CTGF, WNT5A), using the "pROC" package to analyze the hub genes for the diagnostic value of endometriosis. The difference in the importance of hub genes for the diagnosis of endometriosis was analyzed by machine learning random forest, and the combined diagnostic value of hub genes was analyzed by using the Support Vector Machine (SVM) algorithm. The eutopic (EU) and ectopic endometrium (EC) immune microenvironment of endometriosis was evaluated using CIBERSORT, the correlation of hub genes to the immune microenvironment was analyzed. The hub genes associated with AIDGs are differentially expressed in EC and EU of endometriosis and possess important value for the diagnosis of endometriosis. The hub genes have a very important impact on the immune microenvironment of endometriosis, which is important for exploring the connection between endometriosis and autoimmune diseases and provides a new insight for the subsequent study of immunotherapy and diagnosis of endometriosis.

Yang YT ，Jiang XY ，Xu HL ，Chen G ，Wang SL ，Zhang HP ，Hong L ，Jin QQ ，Yao H ，Zhang WY ，Zhu YT ，Mei J ，Tian L ，Ying J ，Hu JJ ，Zhou SG ... -  《International Journal of General Medicine》

Identification of immune- and autophagy-related genes and effective diagnostic biomarkers in endometriosis: a bioinformatics analysis.

To identify autophagy- and immune-related hub genes affecting the diagnosis and treatment of endometriosis. Gene expression data were downloaded from the Gene Expression Omnibus (GEO) (GSE11691 and GSE120103 for training, and GSE7305 for validation). By overlapping the differentially expressed genes (DEGs), Weighted gene co-expression network analysis (WGCNA) module genes, and autophagy-related genes (ARGs), and immune-related genes (IRGs) separately, hub genes were identified using the least absolute shrinkage and selection operator (LASSO)and support vector machine recursive feature elimination (SVM-RFE). The hub genes were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A hub gene-prediction model was constructed and assessed using five-fold cross-validation via five supervised machine-learning algorithms: random forest, the sequential minimal optimization (SMO), K-nearest neighbours (IBK), C4.5 decision tree (J48), and logistics regression. The area under the receiver operating characteristic curve (AUC) was adopted to assess the identification ability of characteristic genes. 1,116 DEGs were obtained from the training cohort, and 22 endometriosis-related IRGs were identified by overlapping the 1,116 DEGs, 3,222 module genes, and 1,793 IRGs. Meanwhile, 45 endometriosis-related ARGs were obtained (1,928 ARGs). Subsequently, nine IRG hub genes (BST2, CCL13, CD86, CSF1, FAM3C, GREM1, ISG20, PSMB8, and S100A11) and nine ARG hub genes (GSK3A, HTR2B, RAB3GAP1, ARFIP2, BNIP3, CSF1, MAOA, PPP1R13L, and SH3GLB2) were obtained by LASSO and SVM-RFE. GO analysis indicated that the ARG hub genes responded to the regulation of autophagy and mitochondrial outer membrane permeabilization, and KEGG enrichment analysis involved serotonergic and dopaminergic synapses. GO analysis also indicated that the IRG hub genes responded to the regulation of leukocyte proliferation and mononuclear cell migration, and KEGG analysis showed enrichment involved in viral protein interaction with cytokines and cytokine receptors. The AUC of the random-forest algorithm of ARGs was 0.975 in the training cohort and 0.940 in the validation cohort, and the AUC of the SMO algorithm of IRGs was 0.907 in the training cohort and 0.8 in the validation cohort. Seventeen hub genes are closely associated with endometriosis. These genes are potential autophagy- and immune-related biomarkers for diagnosis and treatment of endometriosis.

Ji X ，Huang C ，Mao H ，Zhang Z ，Zhang X ，Yue B ，Li X ，Wu Q ... -  《-》

Identification of Key Genes and Pathways associated with Endometriosis by Weighted Gene Co-expression Network Analysis.

Background: Endometriosis is a common gynecological disorder with high rates of infertility and pelvic pain. However, its pathogenesis and diagnostic biomarkers remain unclear. This study aimed to elucidate potential hub genes and key pathways associated with endometriosis in ectopic endometrium (EC) and eutopic endometrium (EU). Material and Method: EC and EU-associated microarray datasets were obtained from the gene expression omnibus (GEO) database. Gene set enrichment analysis was performed to obtain further biological insight into the EU and EC-associated genes. Weighted gene co-expression network analysis (WGCNA) was performed to find clinically significant modules of highly-correlated genes. The hub genes that belong to both the weighted gene co-expression network and protein-protein interaction (PPI) network were identified using a Venn diagram. Results: We obtained EC and EU-associated microarray datasets GSE7305 and GSE120103. Genes in the EC were mainly enriched in the immune response and immune cell trafficking, and genes in the EU were mainly enriched in stress response and steroid hormone biosynthesis. PPI networks and weighted gene co-expression networks were constructed. An EC-associated blue module and an EU-associated magenta module were identified, and their function annotations revealed that hormone receptor signaling or inflammatory microenvironments may promote EU passing through the oviducts and migrating to the ovarian surfaces, and adhesion and immune correlated genes may induce the successful ectopic implantation of the endometrium (EC). Twelve hub genes in the EC and sixteen hub genes in the EU were recognized and further validated in independent datasets. Conclusion: Our study identified, for the first time, the hub genes and enrichment pathways in the EC and EU using WGCNA, which may provide a comprehensive understanding of the pathogenesis of endometriosis and have important clinical implications for the treatment and diagnosis of endometriosis.

Wu J ，Fang X ，Xia X 《International Journal of Medical Sciences》

Analysis of characteristic genes and ceRNA regulation mechanism of endometriosis based on full transcriptional sequencing.

Xie C ，Yin Z ，Liu Y 《Frontiers in Genetics》

Common and specific gene signatures among three different endometriosis subtypes.

To identify the common and specific molecular mechanisms of three well-defined subtypes of endometriosis (EMs): ovarian endometriosis (OE), peritoneal endometriosis (PE), and deep infiltrating endometriosis (DIE). Four microarray datasets: GSE7305 and GSE7307 for OE, E-MTAB-694 for PE, and GSE25628 for DIE were downloaded from public databases and conducted to compare ectopic lesions (EC) with eutopic endometrium (EU) from EMs patients. Differentially expressed genes (DEGs) identified by limma package were divided into two parts: common DEGs among three subtypes and specific DEGs in each subtype, both of which were subsequently performed with the Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed by common DEGs and five hub genes were screened out from the PPI network. Besides, these five hub genes together with selected interested pathway-related genes were further validated in an independent OE RNA-sequencing dataset GSE105764. A total of 54 EC samples from three EMs subtypes (OE, PE, DIE) and 58 EU samples were analyzed, from which we obtained 148 common DEGs among three subtypes, and 729 specific DEGs in OE, 777 specific DEGs in PE and 36 specific DEGs in DIE. The most enriched pathway of 148 shared DEGs was arachidonic acid (AA) metabolism, in which most genes were up-regulated in EC, indicating inflammation was the most common pathogenesis of three subtypes. Besides, five hub genes AURKB, RRM2, DTL, CCNB1, CCNB2 identified from the PPI network constructed by 148 shared DEGs were all associated with cell cycle and mitosis, and down-regulated in EC, suggesting a slow and controlled proliferation in ectopic lesions. The KEGG pathway analysis of specific DEGs in each subtype revealed that abnormal ovarian steroidogenesis was a prominent feature in OE; OE and DIE seems to be at more risk of malignant development since both of their specific DEGs were enriched in the pathways in cancer, though enriched genes were different, while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment. By integrated bioinformatic analysis, we explored common and specific molecular signatures among different subtypes of endometriosis: activated arachidonic acid (AA) metabolism-related inflammatory process and a slow and controlled proliferation in ectopic lesions were common features in OE, PE and DIE; OE and DIE seemed to be at more risk of malignant development while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment, all of which could deepen our perception of endometriosis.

Jiang L ，Zhang M ，Wang S ，Han Y ，Fang X ... -  《PeerJ》